Apply ruff/flake8-import-conventions rule ICN001

ICN001 `seaborn` should be imported as `sns`

Author: DimitriPapadopoulos

docs/notebooks/Paper-v1.0.ipynb

@@ -16,9 +16,9 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import seaborn as sn\n",
+    "import seaborn as sns\n",
     "\n",
-    "sn.set(style='whitegrid')"
+    "sns.set(style='whitegrid')"
    ]
   },
   {
@@ -192,7 +192,7 @@
     "        ax = plt.gca()\n",
     "\n",
     "    outer_score = data.loc[data[score].notnull(), [score, 'zscored']]\n",
-    "    sn.distplot(\n",
+    "    sns.distplot(\n",
     "        outer_score.loc[outer_score.zscored == zscored, score],\n",
     "        hist=True,\n",
     "        norm_hist=True,\n",
@@ -318,12 +318,12 @@
    },
    "outputs": [],
    "source": [
-    "sn.set(style='whitegrid')\n",
+    "sns.set(style='whitegrid')\n",
     "\n",
     "fig = plt.figure(figsize=(20, 8))\n",
     "ax1 = plt.subplot2grid((2, 4), (0, 0), colspan=2, rowspan=2)\n",
     "\n",
-    "sn.violinplot(\n",
+    "sns.violinplot(\n",
     "    x='Classifier',\n",
     "    y='AUC',\n",
     "    hue='Split scheme',\n",
@@ -390,8 +390,8 @@
    "outputs": [],
    "source": [
     "zscoreddf = loso_outer.loc[loso_outer.zscored == 0, ['auc', 'acc', 'site']]\n",
-    "palette = sn.color_palette('cubehelix', len(set(zscoreddf.site)))\n",
-    "sn.pairplot(\n",
+    "palette = sns.color_palette('cubehelix', len(set(zscoreddf.site)))\n",
+    "sns.pairplot(\n",
     "    zscoreddf.loc[zscoreddf.auc.notnull(), ['auc', 'acc', 'site']], hue='site', palette=palette\n",
     ")"
    ]
@@ -405,12 +405,12 @@
    "outputs": [],
    "source": [
     "sites = sorted(set(loso_outer.site.ravel().tolist()))\n",
-    "palette = sn.color_palette('husl', len(sites))\n",
+    "palette = sns.color_palette('husl', len(sites))\n",
     "fig = plt.figure()\n",
     "for i, site in enumerate(sites):\n",
     "    sitedf = loso_outer.loc[loso_outer.site == site]\n",
     "    accdf = sitedf.loc[sitedf.zscored == 0]\n",
-    "    sn.distplot(accdf.acc.values.ravel(), bins=20, kde=0, label=site, color=palette[i])\n",
+    "    sns.distplot(accdf.acc.values.ravel(), bins=20, kde=0, label=site, color=palette[i])\n",
     "\n",
     "fig.gca().legend()\n",
     "fig.gca().set_xlim([0.5, 1.0])"

docs/notebooks/Paper-v2.0.ipynb

@@ -310,7 +310,7 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sn\n",
+    "import seaborn as sns\n",
     "\n",
     "rfc_acc = [\n",
     "    0.842,\n",
@@ -392,13 +392,13 @@
     "\n",
     "fig = plt.figure(figsize=(10, 3))\n",
     "ax2 = plt.subplot2grid((1, 4), (0, 3))\n",
-    "plot = sn.violinplot(\n",
+    "plot = sns.violinplot(\n",
     "    data=df, x='Model', y='accuracy', ax=ax2, palette=colors, bw=0.1, linewidth=0.7\n",
     ")\n",
     "for i in range(dim):\n",
     "    ax2.axhline(np.average(allvals[i]), ls='--', color=colors[i], lw=0.8)\n",
     "#     ax2.axhline(np.percentile(allvals[i], 50), ls='--', color=colors[i], lw=.8)\n",
-    "# sn.swarmplot(x=\"model\", y=\"accuracy\", data=df, color=\"w\", alpha=.5, ax=ax2);\n",
+    "# sns.swarmplot(x=\"model\", y=\"accuracy\", data=df, color=\"w\", alpha=.5, ax=ax2);\n",
     "ax2.yaxis.tick_right()\n",
     "ax2.set_ylabel('')\n",
     "ax2.set_xticklabels(ax2.get_xticklabels(), rotation=40)\n",
@@ -507,7 +507,7 @@
     "\n",
     "fig = plt.figure(figsize=(10, 3))\n",
     "ax2 = plt.subplot2grid((1, 4), (0, 3))\n",
-    "plot = sn.violinplot(data=df, x='Model', y='auc', ax=ax2, palette=colors, bw=0.1, linewidth=0.7)\n",
+    "plot = sns.violinplot(data=df, x='Model', y='auc', ax=ax2, palette=colors, bw=0.1, linewidth=0.7)\n",
     "for i in range(dim):\n",
     "    ax2.axhline(np.average(allvals[i]), ls='--', color=colors[i], lw=0.8)\n",
     "\n",
@@ -585,10 +585,10 @@
    },
    "outputs": [],
    "source": [
-    "import seaborn as sn\n",
+    "import seaborn as sns\n",
     "from sklearn.externals.joblib import load as loadpkl\n",
     "\n",
-    "sn.set_style('white')\n",
+    "sns.set_style('white')\n",
     "\n",
     "# Get the RFC\n",
     "estimator = loadpkl(\n",
@@ -667,7 +667,7 @@
     "        df['Importance'] += [tree.feature_importances_[i]]\n",
     "fig = plt.figure(figsize=(20, 6))\n",
     "# plt.title(\"Feature importance plot\")\n",
-    "sn.boxplot(x='Feature', y='Importance', data=pd.DataFrame(df), linewidth=1, notch=True)\n",
+    "sns.boxplot(x='Feature', y='Importance', data=pd.DataFrame(df), linewidth=1, notch=True)\n",
     "plt.xlabel(f'Features selected ({len(features)})')\n",
     "# plt.bar(range(nft), importances[indices],\n",
     "#        color=\"r\", yerr=std[indices], align=\"center\")\n",